Public Institutions Features.These features include school fees, donations, payment to church etc. They are returned in the kenya/features/public-institutions endpoint.Definitions of these features generated by Pngme are:
| Feature | Feature Definition | Use Case | Value Proposition | Return Value | |
|---|---|---|---|---|---|
| count_public_institutions _events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through public institutions, where public institutions are broken down into general public institutions, charity, church, government and school events. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations for public institution such as charity and churches based on their past donations and interests • Improve customer satisfaction and loyalty by offering loans for school fees pyment at the needed time • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | • These features may be indicative of individuals belonging to a religious community and inclined towards charitable giving. It may also suggest individuals with dependents in educational institutions or those pursuing education themselves. • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_charity_events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through charity events. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations for public institution such as charity • Improve data quality and reliability by using SMS data as a source of truth for verifying charity events of customers. | • This feature may be indicative of individuals inclined towards charitable giving. • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_church _events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through church events. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations for public institution such as churches based on their past donations and interests • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | • This feature may be indicative of individuals belonging to a religious community • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_government _events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through government events eg E-CITIZEN, county government. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| count_school _events_{t0_days}_{t1_days} | The number of SMS received indicating that the user engaged in spending through school events. The counts are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer satisfaction and loyalty by offering loans for school fees payment at the needed time • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | It may suggest individuals with dependents in educational institutions or those pursuing education themselves. • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {int, null} | |
| sum_of_public_institutions _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through public institutions, where public institutions are broken down into general public institutions, charity, church, government and school spends. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations for public institution such as charity and churches based on their past donations and interests • Improve customer satisfaction and loyalty by offering loans for school fees payment at the needed time • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution spends of customers. | • This feature may be indicative of individuals belonging to a religious community and inclined towards charitable giving. It may also suggest individuals with dependents in educational institutions or those pursuing education themselves. • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_charity_debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through charity, The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations for public institution such as charity • Improve data quality and reliability by using SMS data as a source of truth for verifying charity events of customers. | • This feature may be indicative of individuals inclined towards charitable giving. • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_church _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through church. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations for public institution such as churches based on their past donations and interests • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | • This feature may be indicative of individuals belonging to a religious community • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_government _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through government eg E-CITIZEN, county government. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | •Increase customer engagement and retention by personalized recommendations • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} | |
| sum_of_school _debits_{t0_days}_{t1_days} | The sum of debits where user engaged in spending through school. The debits are summed over a period of t0 to t1 days history prior to the prediction date, where the time windows are 0-30, 31-90, or 0-90 days. | • Improve customer satisfaction and loyalty by offering loans for school fees payment at the needed time • Improve data quality and reliability by using SMS data as a source of truth for verifying public institution events of customers. | It may suggest individuals with dependents in educational institutions or those pursuing education themselves. • Understanding a user’s activity related to public institution events can help tailor marketing strategies and develop products that cater to this specific customer segment. | {float, null} |